{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maximum Likelihood Estimation algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script is a example of the Maximum Likelihood Estimation algorithm for energy disaggregation. \n",
    "\n",
    "It is liable when a single disaggregated appliance is desired instead of disaggregating many. Beside, the appliance should be mostly resistive to achieve accuracy. \n",
    "\n",
    "It is based on paired Events: \n",
    "    - OnPower: delta value when the appliance is turned on.\n",
    "    - OffPOwer: delta value when the appliance is turned off.\n",
    "    - Duration: duration between onpower and offpower. \n",
    "    \n",
    "Also, to discard many unlikely events, three constrains are set: \n",
    "    - PowerNoise: mininum delta value below which the delta is considered noise.\n",
    "    - PowerPair: maximum difference between OnPower and OffPower (considering appliances with constans energy consumption). \n",
    "    - timeWindow: maximum time frame between Onpower and Offpower. \n",
    "    \n",
    "Features aforementioned are modeled with Gaussian, Gaussian Mixtures or Poisson. For each incoming paired event, the algorithm will extract these three features and will evaluate the maximum likelihood probability for that paired event of being a certain appliance. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from os.path import join\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = (13, 6)\n",
    "#plt.style.use('ggplot')\n",
    "from datetime import datetime as datetime2\n",
    "from datetime import timedelta\n",
    "\n",
    "import nilmtk\n",
    "from nilmtk.disaggregate.maximum_likelihood_estimation import MLE\n",
    "from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore\n",
    "from scipy.stats import poisson, norm\n",
    "from sklearn import mixture\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_all_appliances(appliance):\n",
    "\n",
    "    # Filtering by appliances: \n",
    "    print \"Fetching \" + appliance + \" over data loaded to nilmtk.\"\n",
    "    metergroup = nilmtk.global_meter_group.select_using_appliances(type=appliance)\n",
    "    \n",
    "    if len(metergroup.appliances) == 0: \n",
    "        print \"None \" + appliance + \" found on memory.\"\n",
    "        pass\n",
    "\n",
    "    # Selecting only single meters: \n",
    "    print \"Filtering to get one meter for each \" + appliance\n",
    "\n",
    "    meters = [meter for meter in metergroup.meters if (len(meter.appliances) == 1)]\n",
    "    metergroup = MeterGroup(meters)\n",
    "    print metergroup\n",
    "    print \"Found \" + str(len(metergroup.meters)) + \" \" + appliance\n",
    "\n",
    "    return metergroup\n",
    "\n",
    "\n",
    "def get_all_trainings(appliance, train):\n",
    "\n",
    "    # Filtering by appliances: \n",
    "    print \"Fetching \" + appliance + \" over data train data.\"\n",
    "    elecs = []\n",
    "    for building in train.buildings: \n",
    "        print \"Building \" + str(building) + \"...\"\n",
    "        elec = train.buildings[building].elec[appliance]\n",
    "        if len(elec.appliances) == 1: \n",
    "            print elec\n",
    "            print \"Fetched elec.\"\n",
    "            elecs.append(elec)\n",
    "\n",
    "        else: \n",
    "            print elec\n",
    "            print \"Groundtruth does not exist. Many appliances or None\"\n",
    "\n",
    "    metergroup = MeterGroup(elecs)\n",
    "\n",
    "    return metergroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = '../../../nilmtk/data/ukdale'\n",
    "ukdale = train = DataSet(join(path, 'ukdale.h5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And spliting into train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 5 buildings\n"
     ]
    }
   ],
   "source": [
    "train = DataSet(join(path, 'ukdale.h5'))\n",
    "test = DataSet(join(path, 'ukdale.h5'))\n",
    "train.set_window(end=\"17-5-2013\")\n",
    "test.set_window(start=\"17-5-2013\")\n",
    "#zoom.set_window(start=\"17-5-2013\")\n",
    "print('loaded ' + str(len(ukdale.buildings)) + ' buildings')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The selected appliance might not be trained from ElecMeters where other appliances are presented as we can extract the groundtruth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching kettle over data train data.\n",
      "Building 1...\n",
      "ElecMeter(instance=10, building=1, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1), Appliance(type='food processor', instance=1), Appliance(type='toasted sandwich maker', instance=1)])\n",
      "Groundtruth does not exist. Many appliances or None\n",
      "Building 2...\n",
      "ElecMeter(instance=8, building=2, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1)])\n",
      "Fetched elec.\n",
      "Building 3...\n",
      "ElecMeter(instance=2, building=3, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1)])\n",
      "Fetched elec.\n",
      "Building 4...\n",
      "ElecMeter(instance=3, building=4, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1), Appliance(type='radio', instance=1)])\n",
      "Groundtruth does not exist. Many appliances or None\n",
      "Building 5...\n",
      "ElecMeter(instance=18, building=5, dataset='UK-DALE', appliances=[Appliance(type='kettle', instance=1)])\n",
      "Fetched elec.\n"
     ]
    }
   ],
   "source": [
    "# Appliance to disaggregate: \n",
    "applianceName = 'kettle'\n",
    "# Groundtruth from the training data: \n",
    "metergroup = get_all_trainings(applianceName,train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLE algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " mle = MLE()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we update the model parameter with some guessing values. \n",
    "\n",
    "First guess for features: onpower and offpower gaussian mixtures and duration poisson. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating model\n",
      "{'resistive': True, 'appliance': 'kettle', 'sampling_method': 'first', 'sample_period': '10S', 'thLikelihood': 1e-10, 'timeWindow': 400, 'units': ('power', 'active'), 'thDelta': 1500, 'powerNoise': 50, 'powerPair': 100}\n"
     ]
    }
   ],
   "source": [
    "# setting parameters in the model: \n",
    "mle.update(appliance=applianceName, resistive=True, units=('power','active'), thDelta= 1500, thLikelihood= 1e-10, powerNoise= 50, powerPair= 100, timeWindow= 400, sample_period= '10S', sampling_method='first')\n",
    "\n",
    "# Settings the features parameters by guessing:  \n",
    "mle.onpower = {'name':'gmm', 'model': mixture.GMM(n_components=2)}\n",
    "mle.offpower = {'name':'gmm', 'model': mixture.GMM(n_components=2)}\n",
    "mle.duration = {'name':'poisson', 'model': poisson(0)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train the model with all ocurrences of that model of appliance found on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('kettle', 1)\n",
      "Training on chunk\n",
      "Samples of onpower: 214\n",
      "Samples of offpower: 214\n",
      "Samples of duration: 214\n",
      "Training onpower\n",
      "Training offpower\n",
      "Training duration\n",
      "('kettle', 2)\n",
      "Training on chunk\n",
      "Samples of onpower: 92\n",
      "Samples of offpower: 92\n",
      "Samples of duration: 92\n",
      "Training onpower\n",
      "Training offpower\n",
      "Training duration\n",
      "('kettle', 3)\n",
      "Chunk empty\n"
     ]
    }
   ],
   "source": [
    "mle.train(metergroup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we visualize features with featureHist_colors() to see the distribution and how many samples we have for each appliance (same model from different houses). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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jsQURRJwPrExpa4FOotnTPGBdnX27M+s9DJ7uW5IkSdJgC9NSurIDiUnAOcCdwBmZ9MuB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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b7fdd0d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mle.featuresHist_colors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, we have more events from some houses than others, as we see on the figure above. Therefore, we need to crop information to keep the same number of samples for everyhouse. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retraining onpower\n",
      "Retraining offpower\n",
      "Retraining duration\n"
     ]
    }
   ],
   "source": [
    "mle.no_overfitting()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JkupUdCAxGfg5cBJRMiFJkiSpBRTZ/etWwCXAhcCiPt7vrJjvSpMkScNTI/2/b2YuZOzq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\nSZIkSZIkqZb/HxsnQA0bvt/WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b7f56ce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mle.featuresHist_colors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is another visualization tool to see how model distributions fit over the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d1vPyyzlkHDKf26a/CVwCPIRjFDAK+LL0hwySTHwlEp8C6Z6uLSIihRwZwJ5A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\nPO+wJKmrMJHotqqkP3K3Vqm/cSWuY3npsEQv4BzgdOAIEv9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b7ecae090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mle.featuresHist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Onpower and Offpower seem to fit well with the data but we need to change the model for duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mle.duration = {'name':'gmm', 'model': mixture.GMM(n_components=10)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we retrain the model and use no_overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('kettle', 1)\n",
      "Training on chunk\n",
      "Samples of onpower: 214\n",
      "Samples of offpower: 214\n",
      "Samples of duration: 214\n",
      "Training onpower\n",
      "Training offpower\n",
      "Training duration\n",
      "('kettle', 2)\n",
      "Training on chunk\n",
      "Samples of onpower: 92\n",
      "Samples of offpower: 92\n",
      "Samples of duration: 92\n",
      "Training onpower\n",
      "Training offpower\n",
      "Training duration\n",
      "('kettle', 3)\n",
      "Chunk empty\n",
      "Retraining onpower\n",
      "Retraining offpower\n",
      "Retraining duration\n"
     ]
    },
    {
     "data": {
      "image/png": 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d1vPyyzlkHDKf26a/CVwCPIRjFDAK+LL0hwySTHwlEp8C6Z6uLSIihRwZwJ5A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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b7f6c18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mle.train(metergroup)\n",
    "mle.no_overfitting()\n",
    "mle.featuresHist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the final model distribution for each feature. We need to the integrity of each distribution. Each CDF has to be bounded by one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Onpower cdf: 0.986177684776\n",
      "Offpower cdf: 1.0\n",
      "Duration cdf: 0.987375070732\n"
     ]
    }
   ],
   "source": [
    "mle.check_cdfIntegrity(step=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disaggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Building to disaggregate: \n",
    "building = 2\n",
    "mains = test.buildings[building].elec.mains()\n",
    "\n",
    "# File to store the disaggregation\n",
    "filename= '/home/energos/Escritorio/ukdale-disag-ml.h5'\n",
    "output = HDFDataStore(filename, 'w')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step will take a few minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25656 events found.\n",
      "12419 onEvents found\n",
      "4244 onEvents no paired.\n",
      "1 chunks disaggregated\n"
     ]
    }
   ],
   "source": [
    "mle.disaggregate(mains, output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also receive some information such as total number of events, number of onpower events, number of onevents that have not been paired and chunks disaggregated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing disaggregation with the groundtruth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PYABTGh/XROjEr2CnrXxSLcU7YPRB5BsGaqi8LihqJyJKkzYjBDvKtQAhF+Va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\nURRFURRFURRFURRFURRFUZoq/w9hqtb27P1nfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b7efde790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Groundtruth\n",
    "kettle = test.buildings[building].elec.select_using_appliances(type=applianceName)\n",
    "output.load(mains.key).next().plot()\n",
    "kettle.plot()\n",
    "output.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
